The Newest AI Computing Tool: People

A USC Information Sciences Institute researcher thinks she has
found a new source of artificial intelligence computing power to
solve difficult IT problems of information classification,
reliability, and meaning.

That tool, according to ISI computer scientist Kristina
Lerman, is people, human intelligence at work on the social
web, the network of blogs, bookmark, photo and video-
sharing sites, and other meeting places now involving
hundreds of thousands of individuals daily, recording
observations and sharing opinions and information.

Lerman shared her recent work with others in the growing
field of social information processing a special AAAI-
sponsored symposium on the subject in the March at
Stanford.

She says that extracting 'metadata' about transactions —
who is talking to whom, who is listening, how conclusions are
reached, and how they spread — can help researchers
answer currently refractory problems about documents:
their accuracy and quality, their categorization, the relation
of their embedded terminology.

One benefit, according to Lerman, who in addition to her ISI
appointment, is a research assistant professor at the Viterbi
School of Engineering Department of Computer Science at
University of Southern California, is automatic determination
of the semantics of content from one kind of metadata:
tags.

Tags play a crucial role in a long running project called the
Semantic Web.

For about a decade, she notes, researchers sought a way to
organize data so that someone searching for a specific kind
of "check" wouldn't have to weed out unwanted references
to chess, symbols, verification procedures, financial
documents, political science theories and many more.

But with natural language being as complex as it is, making
sense of tags is not easy. Attempts to manually attack the
vocabulary and build in the intricate interconnections that
signal different word meanings have proved frustrating.

Lerman hopes she's onto another way. Hundreds of
thousands of users are now online, chattering away on all
kinds of topics. This volume of directed discourse provides a
new way to extracting meaning from tags -statistical
models.

The process has been called "folksonomy," a collectively
constructed informal classification system. Unlike the
traditional approach to the Semantic Web, in which a few
knowledge professionals try to agree on a formal
classification system which will then be used to annotate
data, folksonomy emerges from collective tagging activities
of many individuals.

New social websites aimed at sharing information such as
del.icio.us and Flickr organically grow ways for site members
to access each other's holdings. Typically, the members
themselves spontaneously create a tagging system,
encouraged by the site architecture.

The tags emerging from such systems, Lerman and
collaborators have found, can be turned to broader
purposes.

One of Lerman's initial tagging investigations used the photo-
sharing site Flickr, analyzing results returned by a request
for images of 'beetles,' including some pictures of insects,
some pictures of Volkswagens, and a few other entries.

By extracting the tags that Flickr users had described the
images with, and applying a mathematical technique called
the "Expectation-maximization (EM) algorithm," Lerman
found it possible to quite accurately separate pictures of
insects from pictures of cars returned by the "beetle"
search.

Lerman has gone beyond tagging to using metadata to
acquire more and more accurate information about the
content of documents in social networking situations.

"The rise of the social media sites, such as blogs, wikis, Digg
and Flickr among others, underscores the transformation of
the Web to a participatory medium in which users are
collaboratively creating, evaluating and distributing
information," wrote Lerman in a recent paper accepted for
publication in IEEE Internet Computing,

"The innovations introduced by social media have lead to a
new paradigm for interacting with information, what we call
'social information processing'."

In the paper, entitled "Social Information Processing in
Social News Aggregation," Lerman shows by tracking stories
over time, " that social networks play an important role in
document recommendation." In addition to providing a
platform for document recommendation, social Web enables
researchers to study collective user behavior
quantitatively.

In the same paper, Lerman also presented a mathematical
model of how collaborative rating and promotion of stories
emerges from the independent decisions made by many
users. She found good agreement between predictions of the
model and user data gathered from Digg.

In another paper, examining de.licio.us, Lerman and
collaborators "describe a probabilistic model of the user
annotation process," and then used the model "to
automatically find resources relevant to a particular
information domain ... with promising results." Lerman's
collaborators include ISI graduate students Anon
Plangprasopchok, and Chio Wong.

The research was
supported by grants from the National Science Foundation
and DARPA.